{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "# Ignore numpy dtype warnings. These warnings are caused by an interaction\n",
    "# between numpy and Cython and can be safely ignored.\n",
    "# Reference: https://stackoverflow.com/a/40846742\n",
    "warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n",
    "warnings.filterwarnings(\"ignore\", message=\"numpy.ufunc size changed\")\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import nbinteract as nbi\n",
    "\n",
    "sns.set()\n",
    "sns.set_context('talk')\n",
    "np.set_printoptions(threshold=20, precision=2, suppress=True)\n",
    "pd.options.display.max_rows = 7\n",
    "pd.options.display.max_columns = 8\n",
    "pd.set_option('precision', 2)\n",
    "# This option stops scientific notation for pandas\n",
    "# pd.set_option('display.float_format', '{:.2f}'.format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "def df_interact(df):\n",
    "    '''\n",
    "    Outputs sliders that show rows and columns of df\n",
    "    '''\n",
    "    def peek(row=0, col=0):\n",
    "        return df.iloc[row:row + 5, col:col + 8]\n",
    "    interact(peek, row=(0, len(df), 5), col=(0, len(df.columns) - 6))\n",
    "    print('({} rows, {} columns) total'.format(df.shape[0], df.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Quantitative Data \n",
    "\n",
    "We generally use different types of charts to visualize quantitative (numerical) data and qualitative (ordinal or nominal) data.\n",
    "\n",
    "For quantitative data, we most often use histograms, box plots, and scatter plots.\n",
    "\n",
    "We can use the [seaborn plotting library](http://seaborn.pydata.org/) to create these plots in Python. We will use a dataset containing information about passengers aboard the Titanic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import seaborn and apply its plotting styles\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "interactive"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "50b5511f04d74e96a0d018558d71394f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(182 rows, 15 columns) total\n"
     ]
    }
   ],
   "source": [
    "# Load the dataset and drop N/A values to make plot function calls simpler\n",
    "ti = sns.load_dataset('titanic').dropna().reset_index(drop=True)\n",
    "\n",
    "# This table is too large to fit onto a page so we'll output sliders to\n",
    "# pan through different sections.\n",
    "df_interact(ti)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the dataset contains one row for every passenger. Each row includes the age of the passenger and the amount the passenger paid for a ticket. Let's visualize the ages using a histogram. We can use seaborn's `distplot` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8MSe48bwcPumbOHEiJpOJDRs20NLSwqZNm6isrGTSpEl2682ZM4d33nmH/Px8GhsbycrK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\nkgIhhBCiQ1IghBBCdEgKhBBCiA79/66p8Jvkruh5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18ec7518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adding a semi-colon at the end tells Jupyter not to output the\n",
    "# usual <matplotlib.axes._subplots.AxesSubplot> line\n",
    "sns.distplot(ti['age']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, seaborn's `distplot` function will output a smoothed curve that roughly fits the distribution. We can also add a rugplot which marks each individual point on the x-axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EnOD683L4pG/ixImYTCY2bNhAU1MTmzZtory8nEmTJtmtN2fOHN577z1yc3Opr68nIyOD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19037550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ti['age'], rug=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot the distribution itself. Adjusting the number of bins shows that there were a number of children on board."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e3951d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ti['age'], kde=False, bins=30);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box plots\n",
    "\n",
    "Box plots are a convenient way to see where most of the data lie. Typically, we use the 25th and 75th percentiles of the data as the start and endpoints of the box and draw a line within the box for the 50th percentile (the median). We draw two \"whiskers\" that extend to show the the remaining data except outliers, which are marked as individual points outside the whiskers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Xr0pCQsIt/9wgEEmILlxh3LhxUlxcLO+9956MGzdOjhw5ItnZ2eLz+WTIkCEydepU+emn\nn0Tkz9sQo0aNkuXLl0tycrK8+eabIiJSUFAgEyZMkCFDhsiECRNk165d4RwS8JeILlxh+/btMmDA\nAMnJyZGtW7fK9OnTxefzyc6dO2Xbtm1SX18vy5YtC67v9/vl999/lx07dkh2dracPHlSXnzxRXn6\n6adl165d8uyzz8qrr74q+/fvD+OogFtxTxeuEBcXJ/fcc4/ExMSIx+ORGTNmSE5OjkRHR0uvXr0k\nKytLVq9e3eQ106dPl969e4uIyKJFiyQzM1MmT54sIiK9e/eWkpIS2bRp0y3/jiwQTkQXrtO9e3eZ\nNGmSbN68WU6ePCmlpaVy7NixW/5B6Z49ewYfnzp1Sn7++WfZuXNncFkgEJC4uDiz4wZCQXThOhcu\nXJCJEydK//79ZcyYMZKZmSm//vrrLTPdTp06BR/X1dVJdna2TJkypck60dHcQYO7EF24zpdffikd\nO3aUjRs3Bn833YEDB5p9Td++feXMmTPSp0+f4LKPPvpIKioqZO7cuW16vEBrMA2A68TGxorf75cD\nBw7I+fPn5ZNPPpEtW7Y0+bUyN8vJyZHCwkJZv369nDlzRj777DNZtWqV9OjRw/DIgZYx04XrZGRk\nyA8//CCLFi2Suro66d+/v7z++uuyePFiOX369F++JiEhQd555x1ZvXq1rFmzRuLj42X+/Pm33G4A\nwo3fHAEAhri9AACGiC4AGCK6AGCI6AKAIaILAIaILgAYIroAYIjoAoCh/wPWkmBI/rVu4wAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18c16630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='fare', data=ti);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We typically use the Inter-Quartile Range (IQR) to determine which points are considered outliers for the box plot. The IQR is the difference between the 75th percentile of the data and the 25th percentile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60.299999999999997"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower, upper = np.percentile(ti['fare'], [25, 75])\n",
    "iqr = upper - lower\n",
    "iqr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Values greater than 1.5 $\\times$ IQR above the 75th percentile and less than 1.5 $\\times$ IQR below the 25th percentile are considered outliers and we can see them marked indivdiually on the boxplot above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180.44999999999999, -60.749999999999986)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_cutoff = upper + 1.5 * iqr\n",
    "lower_cutoff = lower - 1.5 * iqr\n",
    "upper_cutoff, lower_cutoff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although histograms show the entire distribution at once, box plots are often easier to understand when we split the data by different categories. For example, we can make one box plot for each passenger type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VAgA85dHviNatW6fPP/9cmzdv1uuvv66wsDB3KBWNOgAAuBoeBVH9+vU1dOhQDR06VC6X\nS9u3b9eyZcs0fvx4ORwO7dixw9t1AgCClMfXtu7bt09ff/210tLS9NVXX+n48eOKjY1Vu3btvFkf\nACDIeRREHTt21KlTp9SmTRu1adNG06ZNU+vWrVW1alVv1wcACHIeBVFSUpJSU1O1a9cuValSRWFh\nYQoLC1PLli2N/sEgACDweZQizz77rCTp6NGjSk1N1ZdffqkVK1bo6NGjatWqld58801v1ggACGIe\nPaG1SFRUlOrXr6+6deuqTp06crlcyszM9FZtAIAKwKMR0WuvvabU1FRt27ZNISEhSkhIUI8ePZSc\nnKw6dep4u0YAQBDzKIg++OADdenSRQ8//LBat26tkJAQb9cVVCIiIhUVVUt2u02RkWW/4SnKpmh/\nF00D8G8eBdGqVau8XUdQczgcmjXrr7r++mo6fTqvwt4E1FccDodmzPiLexqAf+Nb6iMOh0OhoaGS\n8kyXUiEQQEDgKNPFCgAAlDeCCABgFEEEADCKIAIAGEUQAQCMIogAAEYRRAAAowgiAIBRBBEAwCiC\nCABgFPdBCUCFOS6P1y24YN2CMmx3Ne8FAFeDIApApzb/76q2O32V2wGAN3FqDgBgFCOiABEREank\n5LlXta3Ldf702rXekZpn+wDwBoIoQDgcDkVH1zZdBgCUO07NAQCMIogAAEYRRAAAowgiAIBRBBEA\nwCiCCABgFEEEADCKIAIAGEUQAQCM4s4KAcDlcikrK7PUdaRrv41PebHbbcrLO62TJ3NUWGh5vF1E\nRKTf9AGAb/CNDwBZWZmaPHmC6TJ8Ijl5LrcyAioYTs0BAIxiRBRgelW/TjXsIcXmnS4s0Jozp664\n3N9dWD+AiocgCjA17CGqGXLloCltOQD4G07NAQCMIogAAEYRRAAAowgiAIBRBBEAwCiCCABgFEEE\nADCKIAIAGEUQAQCMIogAAEYRRH7A5XK5H+OA4MKxBUpHEBnmcrk0ZcpETZkykR9YQYZjC3iGIDIs\nKytTTudxOZ3HS334HQILxxbwDEEEADCKIAIAGEUQAQCMIogAAEYRRAAAowgiAIBRBBEAwCiCCABg\nFEEEADCKIAIAGBXQQTR58mTNmjXrssvee+89DRs2TJI0f/58jR8//rLr7d27V7fddpvXagQAlMxh\nugBv6d27t3r37m26jDLJzHSWaX4wCsS+2u025eWd1smTOSostNzzA7EvgAkBE0RffvmlZs2apYyM\nDN1www364x//KEk6fPiwHnjgAe3YsUN169ZVcnKymjVrphUrVmjJkiVasWJFsXYKCwv1t7/9Tf/8\n5z8VGhqq/v37m+iO24V3ZZ49e3qp6xdYVqnrBJoL++TJPghE3H0buLKAODWXmZmpUaNGaejQoUpL\nS9PEiRM1btw4nTp1Sps3b9aECROUmpqqxo0ba/bs2SW2tWzZMq1bt04rVqzQBx98oO3bt/uoFwCA\nywmIEdHGjRvVoEED9+ile/fuWrRokd544w3dddddio2NlSQlJSWVGkRr167VsGHDVLduXUnS+PHj\ntXXrVu92oAQOxy+H4IknpioyMuqSdTIzne6RQojN5rPafOXCPl1pH/gzu92m8PCwy56aKzpuFx5n\nAMUFxLcjMzNTderUKTYvNjZWVapUUc2aNd3zQkNDVVBQUGJbTqdTtWvXdr+uV69e+RZ7DSIjoxQd\nXbv0FYNYIO6DkBCbIiKqq3LlMyooCL5Tp4C3BcSpuejoaB09erTYvAULFig/P/+q2jpy5Ij79cXt\nAgB8KyCCKDExUYcPH9bq1atVUFCgTz/9VCkpKcrOzi5zW71799aiRYuUkZGhM2fOaN68eV6oGADg\nqYAIouuvv16vvPKKli5dqvbt2+uFF17QSy+9pPDw8DK3NWDAAA0aNEjDhg3THXfcoZYtW3qhYgCA\npwLid0SSFB8fr3feeafYvHbt2hV73a1bN3Xr1k2S1K9fP/Xr10+SNG7cOPc6NptNo0eP1ujRo93z\nHnnkEW+VDQAoRUCMiAAAwYsgAgAYRRABAIwiiAAARhFEAACjCCIAgFEEEQDAKIIIAGBUwPxBa7CK\niIhUVFQt9zSCB8cW8AxBZJjD4dCMGX9xTyN4cGwBz/Dt8AP8kApeHFugdPyOCABgFEEEADCKIAIA\nGEUQAQCMIogAAEYRRAAAowgiAIBRBBEAwCiCCABgFH/2HWBOFxaUOO9yy/1dINYMoPwQRAFmzZlT\n17QcAPwNp+YAAEYxIgoAERGRSk6eW+I6LpdLkv/cZNNutyk8PEwnT+aosNDyeDselwBUPP7xUwsl\ncjgcio6ubbqMMgkJsSkioroqVz6jggLPgwhAxcOpOQCAUQQRAMAogggAYBRBBAAwiiACABhlsyyL\nS5oAAMYwIgIAGEUQAQCMIogAAEYRRAAAowgiAIBRBBEAwCiCCABgFEEEADCKIAIAGEUQ+cCuXbs0\nYMAAxcXFqU+fPtq+fbvpksrNjh071LlzZ/frn3/+WWPGjFGbNm3UtWtXLV++3L3s3LlzmjJlitq3\nb69f/epXWrBggYmSyywtLU0DBw5UmzZtdMcdd+jtt9+WFJx9Xbt2re6++27Fx8frnnvu0ccffywp\nOPtaxOl0KiEhQRs2bJAkHTp0SCNGjFB8fLzuvPNO93yp5P3g715//XW1aNFC8fHx7n9paWn+cWwt\neNXZs2etLl26WEuXLrXOnTtnLV++3OrUqZOVl5dnurRrUlhYaC1fvtxq06aN1b59e/f8cePGWY8/\n/rh19uxZ69tvv7Xat29v7d6927Isy0pOTrZGjBhhnTp1ytq/f7/VrVs365NPPjHVBY+cPHnSateu\nnbV69WqroKDA2rlzp9WuXTtry5YtQdfXjIwMq1WrVtbXX39tWZZlbdmyxWrevLmVmZkZdH290EMP\nPWQ1adLE+vTTTy3Lsqx+/fpZc+bMsc6dO2dt3LjRio+PtzIzMy3LKvnz7e8ee+wx6/XXX79kvj8c\nW0ZEXrZ161bZ7XYNHTpUoaGhGjBggK6//vpi/8sKRAsXLtTixYs1atQo97zs7Gx9/PHHGj9+vCpX\nrqzY2Fj17NnT/T+s9957Tw8//LBq1Kihm266Sffdd5/eeecdU13wyJEjR5SYmKjevXvLbrerefPm\n6tChg7Zt2xZ0fb355pu1ZcsWtW7dWtnZ2Tp27JiqVaumSpUqBV1fiyxbtkxVq1ZVTEyMJGnfvn3a\nu3evxowZo9DQUCUmJqp9+/ZatWpVqZ9vf7d79241bdq02Dx/+c4SRF62f/9+NWzYsNi8m2++Wf/9\n738NVVQ++vfvr9WrV6tly5bueQcPHpTD4VD9+vXd84r6+vPPP8vpdKpRo0aXLPNnTZs21ezZs92v\nf/75Z6WlpUlS0PVVkqpVq6Yff/xRbdu21eTJkzVhwgT98MMPQdnXAwcOKCUlRU8//bR7XkZGhurW\nrasqVaq45xX1p6TPt7/Lzc3VgQMHtHjxYnXq1El333233n33Xb/5zhJEXpaTk6OqVasWm1elShWd\nPXvWUEXlIzo6Wjabrdi8nJycYl9g6Ze+5ubmSlKxfRFo++H06dMaNWqUe1QUrH2NiYnRjh07lJKS\nolmzZunTTz8Nur66XC498cQTmjp1qsLDw93zS/q+lvT59ndOp1OtW7fWkCFDtGHDBj333HNKTk7W\nhg0b/OLYEkReVrVq1UsO3NmzZxUWFmaoIu8pqa9FH/YLlwfSfvjxxx81ePBg1axZUy+++KLCwsKC\ntq8Oh0OhoaFKSEhQUlKSdu7cGXR9ffnll9W0aVMlJiYWm1/SZziQv8v169fXkiVLlJiYqEqVKqlt\n27bq06eP0tLS/OLYEkRedsstt2j//v3F5u3fv7/YcDdY3HjjjXK5XDpy5Ih7XlFfw8PDFRkZWWxf\nXO60pT/67rvvNGjQIHXu3Fkvv/yyqlSpEpR9/eyzz/Tb3/622Lz8/Hw1aNAg6Pq6du1affDBB2rb\ntq3atm2rI0eO6LHHHtP+/ft1+PBhnTt3zr1uUV9LOub+7rvvvtOrr75abF5eXp5iYmL849iW++UP\nKCYvL8/q3LmztXjxYvdVcx07drSys7NNl1Yutm7dWuyqubFjx1qPPfaYlZOT474CZ/v27ZZlWdbM\nmTOt4cOHWydOnHBfgbN27VpTpXvk+PHjVseOHa1XXnnlkmXB1tdjx45Zbdq0sVauXGkVFBRYGzdu\ntFq3bm19//33QdfXi3Xr1s191Vzfvn2tWbNmWXl5edbGjRutuLg468iRI5ZllXzM/VlGRobVsmVL\n68MPP7QKCgqs//znP1ZcXJy1c+dOvzi2BJEP7N6927r33nutuLg4q0+fPtY333xjuqRyc3EQnThx\nwho/frzVrl07KzEx0Vq+fLl7WW5urvWnP/3J6tixo5WQkGAtWLDARMllsmDBAqtx48ZWXFxcsX9/\n/etfg66vlmVZX331ldW3b18rPj7e6tu3r/XFF19YlhV8x/ViFwbRoUOHrJEjR1qtW7e2kpKS3PMt\nq+T94O8++eQTq2fPnlarVq2spKQk68MPP7Qsyz+OLY8KBwAYxe+IAABGEUQAAKMIIgCAUQQRAMAo\ngggAYBRBBAAwiiAC/Fhqaqq6d++u2NhYbdy40XQ5gFc4TBcA4MoWLlyoRo0a6R//+IciIyNNlwN4\nBSMiwI+dOnVKLVq0uOTRBEAwIYgAP9W9e3ft3LlTL730krp3765vv/1Ww4cPV1xcnGJjYzVkyBDt\n2bNH0vlTeJ06ddLMmTPVpk0bTZs2TZK0YcMG9erVS7GxserVq5fef/99k10CLosgAvzUu+++qyZN\nmmjkyJFaunSpHnzwQcXFxWnNmjV66623VFhYqBkzZrjXdzqd+t///qeVK1dq+PDhSk9P16OPPqr7\n779f77//vn73u9/pqaee0meffWawV8Cl+B0R4KciIiIUEhKisLAwORwOPfTQQxo5cqTsdrvq16+v\nvn37at68ecW2efDBB9WgQQNJ0qRJk9SnTx8NHDhQktSgQQNlZGQoJSXlkufwACYRREAAqFWrlgYM\nGKDFixcrPT1d+/fv13fffXfJQ8rq1q3rnv7++++1d+9erVmzxj3P5XIpIiLCZ3UDniCIgABw7Ngx\n9evXT40bN1aXLl3Up08f7du375IRUeXKld3TBQUFGj58uAYPHlxsHbudM/LwLwQREAA++ugjVapU\nSX//+99ls9kkSZs2bSpxm4YNG+rgwYO68cYb3fMWLVqkrKwsTZgwwav1AmXBf42AABAeHi6n06lN\nmzbp0KFDWrZsmZYsWVLskdYXGzlypDZu3KhXXnlFBw8e1Hvvvac5c+YoJibGh5UDpWNEBASAu+++\nW9u2bdOkSZNUUFCgxo0b65lnntHkyZN14MCBy27TokULvfDCC5o3b57mz5+v6OhoTZw48ZJTdYBp\nPKEVAGAUp+YAAEYRRAAAowgiAIBRBBEAwCiCCABgFEEEADCKIAIAGEUQAQCM+j8rv/mjYz+OwQAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a188554a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='fare', y='who', data=ti);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The separate box plots are much easier to understand than the overlaid histogram below which plots the same data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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M8uXLOXjwIHv37sVqtbJt2zaefvppANasWcMjjzzC9u3bWb16NQ6Hg127dgGw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\nJElSiP8P9OOTgkN+Mp0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18942080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ti.loc[ti['who'] == 'woman', 'fare'])\n",
    "sns.distplot(ti.loc[ti['who'] == 'man', 'fare'])\n",
    "sns.distplot(ti.loc[ti['who'] == 'child', 'fare']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Brief Aside on Using Seaborn\n",
    "\n",
    "You may have noticed that the `boxplot` call to make separate box plots for the `who` column was simpler than the equivalent code to make an overlaid histogram. Although `sns.distplot` takes in an array or Series of data, most other seaborn functions allow you to pass in a DataFrame and specify which column to plot on the x and y axes. For example:\n",
    "\n",
    "```python\n",
    "# Plots the `fare` column of the `ti` DF on the x-axis\n",
    "sns.boxplot(x='fare', data=ti);\n",
    "```\n",
    "\n",
    "When the column is categorical (the `'who'` column contained `'woman'`, `'man'`, and `'child'`), seaborn will automatically split the data by category before plotting. This means we don't have to filter out each category ourselves like we did for `sns.distplot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wQcXFxWnNmjV66623VFhYqBkzZrjXdzqd+t///qeVK1dq+PDhSk9P16OPPqr7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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a193ce3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fare (numerical) on the x-axis,\n",
    "# who (nominal) on the y-axis\n",
    "sns.boxplot(x='fare', y='who', data=ti);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plots\n",
    "\n",
    "Scatter plots are used to compare two quantitative variables. We can compare the `age` and `fare` columns of our Titanic dataset using a scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RVVWFxx9/HHfdddeINpIYPkJSxtuH2vDhqU7EEhKSsp4xxstzKAt6sKShFmuXTkVClLBr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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19857e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='age', y='fare', data=ti);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default seaborn will also fit a regression line to our scatterplot and bootstrap the scatterplot to create a 95% confidence interval around the regression line shown as the light blue shading around the line above. In this case, the regression line doesn't seem to fit the scatter plot very well so we can turn off the regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EkplXx7yIA3X1N1OYvTrfG/RBBV26j55dsMBjEHyhb5t6bN3pVgRCEvxBCQws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\nEEJUiQSqEEJUyX/j00JPDPyzzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a524240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='age', y='fare', data=ti, fit_reg=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can color the points using a categorical variable. Let's use the `who` column once more:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QvSelwam8vJx///d/Z/r06SxcuDBWDKuhoYE777yT6dOnM3/+fJ5//vnYPqFQ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\n1y8s0U7pqYYPXEPJe0ZSM3Ew3aKIA178MncOb7tfsFYKcDQ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a194e8a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='age', y='fare', hue='who', data=ti, fit_reg=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this plot we can see that all passengers below the age of 18 or so were marked as `child`. There doesn't seem to be a noticable split between male and female passenger fares, although the two most expensive tickets were purchased by males."
   ]
  }
 ],
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    "name": "ipython",
    "version": 3
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   "name": "python",
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   "title_sidebar": "Contents",
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   "toc_section_display": true,
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  }
 },
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